期刊论文详细信息
BMC Genomics
Genomic regions involved in yield potential detected by genome-wide association analysis in Japanese high-yielding rice cultivars
Masahiro Yano3  Kaworu Ebana5  Hideo Maeda2  Hisatoshi Ohta1  Takuro Ishii3  Hiroshi Tsunematsu3  Yoshinobu Takeuchi3  Hideyuki Hirabayashi3  Kazuki Matsubara3  Eiji Yamamoto4  Toshio Yamamoto5  Hiroshi Kato3  Ritsuko Mizobuchi5  Jun-ichi Yonemaru5 
[1] NARO Tohoku Agricultural Research Center, 3 Yotsusya, Daisen, Akita 014-0102, Japan;NARO Hokuriku Agricultural Research Center, 1-2-1 Inada, Jyoetsu, Niigata 943-0193, Japan;NARO Institute of Crop Science, 2-1-18 Kannondai, Tsukuba, Ibaraki 305-8518, Japan;NARO Institute of Vegetable and Tea Science, 360 Kusawa, Ano, Tsu, Mie 514-2392, Japan;National Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan
关键词: Association mapping;    Single-nucleotide polymorphisms (SNPs);    Introgression;    Japonica;    Indica;    High yield;    Rice;   
Others  :  1217252
DOI  :  10.1186/1471-2164-15-346
 received in 2013-08-29, accepted in 2014-04-28,  发布年份 2014
PDF
【 摘 要 】

Background

High-yielding cultivars of rice (Oryza sativa L.) have been developed in Japan from crosses between overseas indica and domestic japonica cultivars. Recently, next-generation sequencing technology and high-throughput genotyping systems have shown many single-nucleotide polymorphisms (SNPs) that are proving useful for detailed analysis of genome composition. These SNPs can be used in genome-wide association studies to detect candidate genome regions associated with economically important traits. In this study, we used a custom SNP set to identify introgressed chromosomal regions in a set of high-yielding Japanese rice cultivars, and we performed an association study to identify genome regions associated with yield.

Results

An informative set of 1152 SNPs was established by screening 14 high-yielding or primary ancestral cultivars for 5760 validated SNPs. Analysis of the population structure of high-yielding cultivars showed three genome types: japonica-type, indica-type and a mixture of the two. SNP allele frequencies showed several regions derived predominantly from one of the two parental genome types. Distinct regions skewed for the presence of parental alleles were observed on chromosomes 1, 2, 7, 8, 11 and 12 (indica) and on chromosomes 1, 2 and 6 (japonica). A possible relationship between these introgressed regions and six yield traits (blast susceptibility, heading date, length of unhusked seeds, number of panicles, surface area of unhusked seeds and 1000-grain weight) was detected in eight genome regions dominated by alleles of one parental origin. Two of these regions were near Ghd7, a heading date locus, and Pi-ta, a blast resistance locus. The allele types (i.e., japonica or indica) of significant SNPs coincided with those previously reported for candidate genes Ghd7 and Pi-ta.

Conclusions

Introgression breeding is an established strategy for the accumulation of QTLs and genes controlling high yield. Our custom SNP set is an effective tool for the identification of introgressed genome regions from a particular genetic background. This study demonstrates that changes in genome structure occurred during artificial selection for high yield, and provides information on several genomic regions associated with yield performance.

【 授权许可】

   
2014 Yonemaru et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150705223542152.pdf 1239KB PDF download
Figure 4. 125KB Image download
Figure 3. 147KB Image download
Figure 2. 130KB Image download
Figure 1. 54KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】
  • [1]United Nations, Department of Economic and Social Affairs, Population Division: World Population Prospects: The 2010 Revision, Highlights and Advance Tables. Working Paper No. ESA/P/WP.220
  • [2]Hargrove TR, Cabanilla VL: Impact of semi-dwarf varieties on Asian rice-breeding programs. Bioscience 1979, 29(12):731-735.
  • [3]Khush GS: Green revolution: preparing for the 21st century. Genome 1999, 42(4):646-655.
  • [4]Foster KW, Rutger JN: Inheritance of semidwarfism in rice, Oryza sativa L. Genetics 1978, 88(3):559-574.
  • [5]Fan C, Xing Y, Mao H, Lu T, Han B, Xu C, Li X, Zhang Q: GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet 2006, 112(6):1164-1171.
  • [6]Weng J, Gu S, Wan X, Gao H, Guo T, Su N, Lei C, Zhang X, Cheng Z, Guo X, Wang J, Jiang L, Zhai H, Wan J: Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight. Cell Res 2008, 18(12):1199-1209.
  • [7]Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, Yano M: Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet 2008, 40(8):1023-1028.
  • [8]Li Y, Fan C, Xing Y, Jiang Y, Luo L, Sun L, Shao D, Xu C, Li X, Xiao J, He Y, Zhang Q: Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Nat Genet 2011, 43(12):1266-1269.
  • [9]Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, Zeng R, Zhu H, Dong G, Qian Q, Zhang G, Fu X: Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet 2012, 44(8):950-954.
  • [10]Ishimaru K, Hirotsu N, Madoka Y, Murakami N, Hara N, Onodera H, Kashiwagi T, Ujiie K, Shimizu B-i, Onishi A, Miyagawa H, Katoh E: Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat Genet 2013, 45:707-711.
  • [11]Wu W, Zheng XM, Lu G, Zhong Z, Gao H, Chen L, Wu C, Wang HJ, Wang Q, Zhou K, Wang JL, Wu F, Zhang X, Guo X, Cheng Z, Lei C, Lin Q, Jiang L, Wang H, Ge S, Wan J: Association of functional nucleotide polymorphisms at DTH2 with the northward expansion of rice cultivation in Asia. Proc Natl Acad Sci 2013, 110(8):2775-2780.
  • [12]Xue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q: Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat Genet 2008, 40(6):761-767.
  • [13]Kato H: Development of rice varieties for whole crop silage (WCS) in Japan. Jpn Agr Res Q 2008, 42(4):231-236.
  • [14]Ebana K, Yonemaru J-i, Fukuoka S, Iwata H, Kanamori H, Namiki N, Nagasaki H, Yano M: Genetic structure revealed by a whole-genome single-nucleotide polymorphism survey of diverse accessions of cultivated Asian rice (Oryza sativa L.). Breed Sci 2010, 60(4):390-397.
  • [15]Zhao K, Wright M, Kimball J, Eizenga G, McClung A, Kovach M, Tyagi W, Ali ML, Tung CW, Reynolds A, Bustamante CD, McCouch SR: Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS One 2010, 5(5):e10780.
  • [16]Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR: Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2011, 2:467.
  • [17]Xu X, Liu X, Ge S, Jensen JD, Hu F, Li X, Dong Y, Gutenkunst RN, Fang L, Huang L, Li J, He W, Zhang G, Zheng X, Zhang F, Li Y, Yu C, Kristiansen K, Zhang X, Wang J, Wright M, McCouch S, Nielsen R, Wang W: Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nat Biotechnol 2012, 30(1):105-111.
  • [18]Subbaiyan GK, Waters DL, Katiyar SK, Sadananda AR, Vaddadi S, Henry RJ: Genome-wide DNA polymorphisms in elite indica rice inbreds discovered by whole-genome sequencing. Plant Biotechnol J 2012, 10(6):623-634.
  • [19]Huang X, Kurata N, Wei X, Wang Z-X, Wang A, Zhao Q, Zhao Y, Liu K, Lu H, Li W, Guo Y, Lu Y, Zhou C, Fan D, Weng Q, Zhu C, Huang T, Zhang L, Wang Y, Feng L, Furuumi H, Kubo T, Miyabayashi T, Yuan X, Xu Q, Dong G, Zhan Q, Li C, Fujiyama A, Toyoda A, et al.: A map of rice genome variation reveals the origin of cultivated rice. Nature 2012, 490:497-501.
  • [20]Yamamoto T, Nagasaki H, Yonemaru JI, Ebana K, Nakajima M, Shibaya T, Yano M: Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms. BMC Genomics 2010, 11(1):267. BioMed Central Full Text
  • [21]Chen H, He H, Zou Y, Chen W, Yu R, Liu X, Yang Y, Gao YM, Xu JL, Fan LM, Li Y, Li ZK, Deng XW: Development and application of a set of breeder-friendly SNP markers for genetic analyses and molecular breeding of rice (Oryza sativa L.). Theor Appl Genet 2011, 123(6):869-879.
  • [22]Yonemaru J-i, Yamamoto T, Ebana K, Yamamoto E, Nagasaki H, Shibaya T, Yano M: Genome-wide haplotype changes produced by artificial selection during modern rice breeding in Japan. PLoS One 2012, 7(3):e32982.
  • [23]Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Mitsuoka C, Tamiru M, Innan H, Cano L, Kamoun S, Terauchi R: Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 2012, 30(2):174-178.
  • [24]Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, Li W, Guo Y, Deng L, Zhu C, Fan D, Lu Y, Weng Q, Liu K, Zhou T, Jing Y, Si L, Dong G, Huang T, Lu T, Feng Q, Qian Q, Li J, Han B: Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 2011, 44(1):32-39.
  • [25]Yamamoto E, Yonemaru J-i, Yamamoto T, Yano M: OGRO: The Overview of functionally characterized Genes in Rice Online database. Rice 2012, 5(1):26. BioMed Central Full Text
  • [26]Yonemaru J-i, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M: Q-TARO: QTL Annotation Rice Online database. Rice 2010, 3:194-203.
  • [27]Bryan GT, Wu KS, Farrall L, Jia Y, Hershey HP, McAdams SA, Faulk KN, Donaldson GK, Tarchini R, Valent B: A single amino acid difference distinguishes resistant and susceptible alleles of the rice blast resistance gene Pi-ta. Plant Cell 2000, 12(11):2033-2046.
  • [28]Lu L, Yan W, Xue W, Shao D, Xing Y: Evolution and association analysis of Ghd7 in rice. PLoS One 2012, 7(5):e34021.
  • [29]Sun J, Liu D, Wang JY, Ma DR, Tang L, Gao H, Xu ZJ, Chen WF: The contribution of intersubspecific hybridization to the breeding of super-high-yielding japonica rice in northeast China. Theor Appl Genet 2012, 125(6):1149-1157.
  • [30]Khush G: Breaking the yield frontier of rice. GeoJournal 1995, 35(3):329-332.
  • [31]Wisser RJ, Sun Q, Hulbert SH, Kresovich S, Nelson RJ: Identification and characterization of regions of the rice genome associated with broad-spectrum, quantitative disease resistance. Genetics 2005, 169(4):2277-2293.
  • [32]Zhou T, Wang Y, Chen JQ, Araki H, Jing Z, Jiang K, Shen J, Tian D: Genome-wide identification of NBS genes in japonica rice reveals significant expansion of divergent non-TIR NBS-LRR genes. Mol Genet Genomics 2004, 271(4):402-415.
  • [33]Marri PR, Sarla N, Reddy LV, Siddiq EA: Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet 2005, 6(1):33. BioMed Central Full Text
  • [34]Ishimaru K: Identification of a locus increasing rice yield and physiological analysis of its function. Plant Physiol 2003, 133(3):1083-1090.
  • [35]Zhuang JY, Fan YY, Wu JL, Xia YW, Zheng KL: Comparison of the detection of QTL for yield traits in different generations of a rice cross using two mapping approaches. Yi Chuan Xue Bao 2001, 28(5):458-464.
  • [36]Kojima Y, Ebana K, Fukuoka S, Nagamine T, Kawase M: Development of an RFLP-based rice diversity research set of germplasm. Breed Sci 2005, 55(4):431-440.
  • [37]Ebana K, Kojima Y, Fukuoka S, Nagamine T, Kawase M: Development of mini core collection of Japanese rice landrace. Breed Sci 2008, 58(3):281-291.
  • [38]Murray MG, Thompson WF: Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res 1980, 8(19):4321-4325.
  • [39]Kawahara Y, de la Bastide M, Hamilton J, Kanamori H, McCombie W, Ouyang S, Schwartz D, Tanaka T, Wu J, Zhou S, Childs K, Davidson R, Lin H, Quesada-Ocampo L, Vaillancourt B, Sakai H, Lee S, Kim J, Numa H, Itoh T, Buell C, Matsumoto T: Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 2013, 6(1):4. BioMed Central Full Text
  • [40]Sakai H, Lee SS, Tanaka T, Numa H, Kim J, Kawahara Y, Wakimoto H, Yang CC, Iwamoto M, Abe T, Yamada Y, Muto A, Inokuchi H, Ikemura T, Matsumoto T, Sasaki T, Itoh T: Rice Annotation Project Database (RAP-DB): An integrative and interactive database for rice genomics. Plant Cell Physiol 2013, 54(2):e6.
  • [41]Liu K, Muse SV: PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 2005, 21(9):2128-2129.
  • [42]Gao H, Williamson S, Bustamante CD: A Markov chain Monte Carlo approach for joint inference of population structure and inbreeding rates from multilocus genotype data. Genetics 2007, 176(3):1635-1651.
  • [43]Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 2011, 28(10):2731-2739.
  • [44]Tanabata T, Shibaya T, Hori K, Ebana K, Yano M: SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiol 2012, 160(4):1871-1880.
  • [45]Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES: TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 2007, 23(19):2633-2635.
  • [46]Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25(14):1754-1760.
  • [47]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The sequence alignment/map format and SAMtools. Bioinformatics 2009, 25(16):2078-2079.
  • [48]DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011, 43(5):491-498.
  文献评价指标  
  下载次数:4次 浏览次数:6次